ALOS PALSAR: A Pathfinder Mission for Global-Scale Monitoring of the Environment

Rosenqvist, Åke; Shimada, Masanobu; Ito, Nobuyasu; Watanabe, Masahiro

doi:10.1109/tgrs.2007.901027

Cited by 445 publications

(216 citation statements)

References 8 publications

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“…where the Calibration Factor (CF) = −83, and DN is the digital number from each pixel location [54]. In summary, these processing sequences generated a mosaic of geo-coded, orthorectified, terrain-corrected, radiometrically calibrated and normalized PALSAR scenes with a pixel size of 30 m. Two sets of ortho-rectified images were used as reference images in geo-correction (image-to-image registration) of all other PALSAR images (see Dubeau [55] for details), including PALSAR Level 1.5 G images and the 25-m mosaics ( Table 2).…”

Section: Palsarmentioning

confidence: 99%

Mapping the Dabus Wetlands, Ethiopia, Using Random Forest Classification of Landsat, PALSAR and Topographic Data

et al. 2017

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Abstract:The Dabus Wetland complex in the highlands of Ethiopia is within the headwaters of the Nile Basin and is home to significant ecological communities and rare or endangered species. Its many interrelated wetland types undergo seasonal and longer-term changes due to weather and climate variations as well as anthropogenic land use such as grazing and burning. Mapping and monitoring of these wetlands has not been previously undertaken due primarily to their relative isolation and lack of resources. This study investigated the potential of remote sensing based classification for mapping the primary vegetation groups in the Dabus Wetlands using a combination of dry and wet season data, including optical (Landsat spectral bands and derived vegetation and wetness indices), radar (ALOS PALSAR L-band backscatter), and elevation (SRTM derived DEM and other terrain metrics) as inputs to the non-parametric Random Forest (RF) classifier. Eight wetland types and three terrestrial/upland classes were mapped using field samples of observed plant community composition and structure groupings as reference information. Various tests to compare results using different RF input parameters and data types were conducted. A combination of multispectral optical, radar and topographic variables provided the best overall classification accuracy, 94.4% and 92.9% for the dry and wet season, respectively. Spectral and topographic data (radar data excluded) performed nearly as well, while accuracies using only radar and topographic data were 82-89%. Relatively homogeneous classes such as Papyrus Swamps, Forested Wetland, and Wet Meadow yielded the highest accuracies while spatially complex classes such as Emergent Marsh were more difficult to accurately classify. The methods and results presented in this paper can serve as a basis for development of long-term mapping and monitoring of these and other non-forested wetlands in Ethiopia and other similar environmental settings.

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Section: Palsarmentioning

confidence: 99%

Mapping the Dabus Wetlands, Ethiopia, Using Random Forest Classification of Landsat, PALSAR and Topographic Data

et al. 2017

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“…This leads to a distinct forest/non-forest backscatter contrast, which is much stronger when compared to C-band SAR [40,41]. The main limitation for most tropical countries is the low density of medium resolution C-and L-band SAR observations [42]. A small number of images available per year, however, often hampers the rapid detection of deforestation.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection

et al. 2015

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Abstract:To address the need for timely information on newly deforested areas at medium resolution scale, we introduce a Bayesian approach to combine SAR and optical time series for near real-time deforestation detection. Once a new image of either of the input time series is available, the conditional probability of deforestation is computed using Bayesian updating, and deforestation events are indicated. Future observations are used to update the conditional probability of deforestation and, thus, to confirm or reject an indicated deforestation event. A proof of concept was demonstrated using Landsat NDVI and ALOS PALSAR time series acquired at an evergreen forest plantation in Fiji. We emulated a near real-time scenario and assessed the deforestation detection accuracies using three-monthly reference data covering the entire study site. Spatial and temporal accuracies for the fused Landsat-PALSAR case (overall accuracy = 87.4%; mean time lag of detected deforestation = 1.3 months) were consistently higher than those of the Landsat-and PALSAR-only cases. The improvement maintained even for increasing missing data in the Landsat time series.

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“…In this paper, the Band 1 of spatial resolution 250 m and bandwidth 620-670 nm, and Band 2, of spatial resolution of 250 m and bandwidth 842-876 nm is considered as these bands has special features to identify the agriculture and other land covers. In this paper, the MODIS image considered is MODIS/Terra Surface Depending on the different modes, PALSAR acquires image at spatial resolutions ranging from 6.25 to 50 m, with swath widths from 70 to 360 km, and off-nadir looking angles from 9.7 • to 50.8 • [22]. In overlapping areas, PALSAR could reach a temporal resolution higher than the satellite orbit repeat cycle of 46 days.…”

Section: Data Usedmentioning

confidence: 99%

Quality Assessment of Fused Image of Modis and Palsar

Kumar¹,

Singh

2010

PIER B

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Abstract-It is a current need of research to extensively use the freely available satellite images. The most commonly available satellite images are Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR). The problems with these images are their poor spatial resolution that restricts their use in various applications. This restriction may be minimized by application of the fusion techniques where high resolution image will be used to fuse with low resolution images. Another important aspect of fusion of different sensors data as optical and radar images (where both can provide the complimentary information), and the resultant fused image after fusion may give enhanced and useful information that may be beneficial for various applications. Therefore, in this paper an attempt has been made to fuse the full polarimetric Phased Array type L-band SAR (PALSAR) image with MODIS image and assess the quality of fused image. PALSAR image has a advantage of availability of data in four different channels. These four channels are HH (Transmitted horizontal polarization and received also in horizontal polarization), HV (Transmitted horizontal polarization and received vertical polarization), VH (Transmitted vertical polarization and received horizontal polarization) and VV (Transmitted vertical polarization and received vertical polarization), which provides various landcover information. The curvelet based fusion technique has been applied to MODIS Bands 1 and 2 and PALSAR (HH, HV and VV) bands for assessing the effect of fusion in land cover distinction. The three major land covers i.e., agriculture, urban and water are considered for evaluation of fusion of these images for the Roorkee area of India. The results are quite encouraging, and in near future 192Harish Kumar and Singh it may provide a better platform for the maximize the use of MODIS images.

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ALOS PALSAR: A Pathfinder Mission for Global-Scale Monitoring of the Environment

Cited by 445 publications

References 8 publications

Mapping the Dabus Wetlands, Ethiopia, Using Random Forest Classification of Landsat, PALSAR and Topographic Data

Mapping the Dabus Wetlands, Ethiopia, Using Random Forest Classification of Landsat, PALSAR and Topographic Data

A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection

Quality Assessment of Fused Image of Modis and Palsar

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